class_docstr: A Linear Gaussian Bayesian Network is a Bayesian Network, all
  of whose variables are continuous, and where all of the CPDs
  are linear Gaussians.
  
  An important result is that the linear Gaussian Bayesian Networks
  are an alternative representation for the class of multivariate
  Gaussian distributions.
class_docstr_tok: ["A","Linear","Gaussian","Bayesian","Network","is","a","Bayesian","Network",",","all","of","whose","variables","are","continuous",",","and","where","all","of","the","CPDs","are","linear","Gaussians",".","An","important","result","is","that","the","linear","Gaussian","Bayesian","Networks","are","an","alternative","representation","for","the","class","of","multivariate","Gaussian","distributions","."]
docstr: For now, to_markov_model method has not been implemented for LinearGaussianBayesianNetwork.
docstr_tok: ["For","now",",","to_markov_model","method","has","not","been","implemented","for","LinearGaussianBayesianNetwork","."]
code: def to_markov_model(self):
      """
      For now, to_markov_model method has not been implemented for LinearGaussianBayesianNetwork.
      """
      raise NotImplementedError(
          "to_markov_model method has not been implemented for LinearGaussianBayesianNetwork."
      )
  
file_path: pgmpy/pgmpy/models/LinearGaussianBayesianNetwork.py
imports: import networkx
  import numpy
  from pgmpy.factors.continuous import LinearGaussianCPD
  from pgmpy.factors.distributions import GaussianDistribution
  from pgmpy.global_vars import logger
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 9 for calling the LinearGaussianBayesianNetwork obj's underlying member method code with example usage: obj.to_markov_model() without return types